Name
Abstract | ||
|---|---|---|
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facilitate this, we propose a compiler-aware neural pruning search framework to achieve high-speed inference on autonomous vehicles for 2D and 3D object detection. The framework automatically searches the pruning scheme and rate for each layer to find a best-suited pruning for optimizing detection accuracy and speed performance under compiler optimization. Our experiments demonstrate that for the first time, the proposed method achieves (close-to) real-time, 55ms and 97ms inference times for YOLOv4 based 2D object detection and PointPillars based 3D detection, respectively, on an off-the-shelf mobile phone with minor (or no) accuracy loss. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DAC18074.2021.9586163 | 2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC) |
Keywords | DocType | ISSN |
real-time, object detection, autonomous driving | Conference | 0738-100X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pu Zhao | 1 | 32 | 11.73 |
| Geng Yuan | 2 | 9 | 3.80 |
| Yuxuan Cai | 3 | 2 | 2.05 |
| Wei Niu | 4 | 24 | 11.21 |
| Qi Liu | 5 | 17 | 3.67 |
| Wujie Wen | 6 | 0 | 0.34 |
| Bin Ren | 7 | 82 | 18.03 |
| Yanzhi Wang | 8 | 1082 | 136.11 |
| Xue Lin | 9 | 86 | 14.97 |